Virtual Playgrounds and BuddyBots: A Data-minefield for Tweens
Bibliographic record
Abstract
This article examines the online places where tweens play, chat, and hang out. We argue that the vision behind these places is defined by commercial imperatives that seek to embed surveillance deeper and deeper into children’s playgrounds and social interactions. Online marketers do more than implant branded products into a child’s play; they collect the minute details of a child’s life so they can build a ‘‘relationship’’ of ‘‘trust’’ between the child and brand. Although marketing to children is not new, a networked environment magnifies the effect on a child’s identity because it opens up a child’s private online spaces to the eye of the marketer in unprecedented ways. Online marketers accordingly invade the child’s privacy in a profound sense, by artificially manipulating the child’s social environment and communications in order to facilitate a business agenda.\nWe start by examining five of the Web sites that have been identified by tweens as ‘‘favorites’’. Each site contains examples of marketing practices that are typical of virtual playgrounds, and which turn kids’ online play into a continuous feedback loop for market research.\nAfter looking at the places where tweens play, we turn to one of the places where tweens talk. We examine how the principles of human-computer interaction have been used in an instant messaging environment to create virtual ‘‘people’’ that interact with kids, for all intents and purposes, like a real person. By logging the interactions, these BuddyBot programs are able to ‘‘learn’’ about the child and create the illusion of friendship between it and the child. This perfects the relationship between the child and the brand by introducing a virtual person into the equation, a person who is able to give the child ideas about what clothes to wear, what movies to see, what products to buy.\nFinally, we provide a brief overview of American and Canadian legislation dealing with children’s online privacy, and assess whether or not current laws have been able to protect children’s privacy in the online environment. We also examine the ways in which electronic commerce legislation has addressed the role of virtual agents, and assess how well fair information practices can protect kids from the invasive nature of child-bot relationships.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".